Option 1: View demo data
Please wait a couple seconds after clicking and you should be redirected to the Visualize and Explore tab.
Option 2: Upload your own data Files
(Required) Please upload the aggregate report file. Note that this will be the data displayed in the main table in the Explore tab.
(Required) Please upload the corresponding metrics file for the main file that you have chosen.
(Optional) If you would like, you can upload an additional aggregate report file generated with either Class I or Class II results to supplement your main table. (E.g. if you uploaded Class I data as the main table, you can upload your Class II report here as supplemental data)
(Optional) Additionally, you can upload a gene-of-interest list in a tsv format, where each row is a single gene name. These genes (if in your aggregate report) will be highlighted in the Gene Name column.
Basic Instructions: How to explore your data using pVACview?
Step 1: Upload your own data / Load demo data
You can either choose to explore a demo dataset that we have prepared from the HCC1395 cell line, or choose to upload your own datasets.
If you are uploading your own datasets, the two required inputs are output files you obtain after running the pVACseq pipeline. The aggregated tsv file is a list of all predicted epitopes and their binding affinity scores with additional variant information and the metrics json file contains additional transcript and peptide level information.
You have the option of uploading an additional file to supplement the data you are exploring. This includes: additional class I or II information and a gene-of-interest tsv file.
Step 2: Exploring your data
To explore the different aspects of your neoantigen candidates, you will need to navigate to the Aggregate Report of Best Candidate by Variant on the visualize and explore tab. For detailed variant, transcript and peptide information for each candidate listed, you will need to click on the Investigate button for the specific row of interest. This will prompt both the transcript and peptide table to reload with the matching information.
By hovering over each column header, you will be able to see a brief description of the corresponding column and for more details, you can click on the tooltip located at the top right of the aggregate report table.
After investigating each candidate, you can label the candidate using the dropdown menu located at the second to last column of the table. Choices include:
Accept, Reject or Review.
Step 3: Exporting your data
When you have either finished ranking your neoantigen candidates or need to pause and would like to save your current evaluations, you can export the current main aggregate report using the export page.
Navigate to the export tab, and you will be able to name your file prior to downloading in either tsv or excel format. The excel format is user-friendly for downstream visualization and manipulation. However, if you plan on to continuing editing the aggregate report and would like to load it back in pVACview with the previous evaluations preloaded, you will need to download the file in a tsv format. This serves as a way to save your progress as your evaluations are cleared upon closing or refreshing the pVACview app.
Advanced Options: Regenerate Tiering with different parameters
Current version of pVACseq results defaults to positions 1, 2, n-1 and n (for a n-mer peptide) when determining anchor positions. If you would like to use our allele specific anchor results and regenerate the tiering results for your variants, please specify your contribution cutoff and submit for recalculation. More details can be found here.
For further explanations on these inputs, please refer to the pVACview documentation.
Original Parameters for Tiering
These are the original parameters used in the tiering calculations extracted from the metrics data file given as input.
Current Parameters for Tiering
These are current parameters used in the tiering calculations which may be different from the original parameters if candidates were re-tiered.
Add Comments for selected variant
Comment:
Aggregate Report of Best Candidates by Variant
Variant Information
Variant & Gene Info
Additional variant information:
Peptide Evaluation Overview
Transcript and Peptide Set Data
Allele specific anchor prediction heatmap for the candidates in peptide table.
HLA allele specific anchor predictions overlaying candidate peptide sequences for selected transcript set.
Anchor vs Mutation position Scenario Guide
Anchor Positions
Anchor Weights
Additional Peptide Information
Error: Missing required files (both aggregate report and metrics files are required to properly visualize and explore candidates).
Aggregate Report of Best Candidates by Variant
If using pVACview with pVACtools output, the user is required to provide at least the following two files:
all_epitopes.aggregated.tsv
all_epitopes.aggregated.metrics.json
The
all_epitopes.aggregated.tsv
file is an aggregated version of the all_epitopes TSV. It presents
the best-scoring epitope for each variant, along with additional binding affinity, expression, and coverage
information for that epitope. It also gives information about the total number of well-scoring epitopes for each
variant, the number of transcripts covered by those epitopes, and the HLA alleles that those epitopes are
well-binding to. Here, a well-binding or well-scoring epitope is any epitope that has a stronger binding affinity
than the
binding_threshold
described below. Additional epitopes that are not meeting the binding
threshold are included in order to provide users with a wider range of epitopes to investigate that may not strictly
meet all the selected cutoffs. Included peptides are determined by the
aggregate_inclusion_binding_threshold
and the
aggregate_includion_count_limit
and the total number of included epitopes is noted in the
aggregated TSV. The report then bins variants into tiers that offer suggestions about the suitability of variants
for use in vaccines.
The
all_epitopes.aggregated.metrics.json
complements the
all_epitopes_aggregated.tsv
and
is required for the tool's proper functioning. It contains additional metadata for all included peptides that power
additional visualizations.
Column Names in the all_epitopes.aggregated.tsv File: Description
ID
: A unique identifier for the variant.
Index
: A unique identifier for the variant and best neoantigen candidate.
HLA Alleles :
For each HLA allele in the run, the number of this variant’s epitopes that bound well to
the HLA allele (with
lowest
or
median
mutant binding affinity <
binding_threshold
).
Gene
: The Ensembl gene name of the affected gene.
AA Change
: The amino acid change for the mutation.
Num Passing Transcripts
: The number of transcripts coding for this mutation that resulted in at least one
well-binding peptide (
lowest
or
median
mutant binding affinity <
binding_threshold
).
Best Peptide
: The best-binding mutant epitope sequence (lowest binding affinity or percentile depending on
the selected
top_score_metric
and
top_score_metric2
). Epitope sequences that don't have any problematic
positions and that meet the anchor criteria are prioritized. Additionally, epitopes resulting from a transcript that is
protein_coding, that doesn't have any CDS flags, that is the MANE select or Canonical transcript, and that has a TSL below the
maximum_transcript_support_level
are also prioritized.
Best Transcript
: Best transcript coding for the best peptide if multiple transcripts give rise to the best peptide.
Transcripts that are protein_coding, that don't have any CDS flags, that are the MANE select or Canonical transcript, that
have a TSL below the
maximum_transcript_support_level
that are long, and have a high transcript expression are prioritized.
MANE Select
: MANE Select status of the best transcript.
Canonical
: Canonical status of the best transcript.
TSL
: Transcript support level of the best transcript.
Pos
: The one-based position(s) of any amino acids that differ from the matched wildtype.
NA
if there is
no matched wildtype sequence (as can occur downstream of frameshift mutations or long indels).
Prob Pos
: If you specify
problematic_amino_acids
when running pVACseq, the position(s) of problematic
peptides within the best peptide.
Num Included Peptides
: The number of unique included peptides for this mutation.
Num Passing Peptides
: The number of unique well-binding peptides for this mutation.
IC50 MT
:
Lowest
or
Median
ic50 binding affinity of the best-binding mutant epitope
across all prediction algorithms used.
IC50 WT
:
Lowest
or
Median
ic50 binding affinity of the corresponding wildtype epitope
across all prediction algorithms used.
%ile MT
:
Lowest
or
Median
binding affinity percentile rank of the best-binding mutant
epitope across all prediction algorithms used (of those that provide percentile output)
%ile WT
:
Lowest
or
Median
binding affinity percentile rank of the corresponding wildtype
epitope across all prediction algorithms used (of those that provide percentile output)
RNA Expr
: Gene expression value for the annotated gene containing the variant.
RNA VAF
: Tumor RNA variant allele frequency (VAF) at this position.
Allele Expr
: RNA Expr * RNA VAF
RNA Depth
: Tumor RNA depth at this position.
DNA VAF
: Tumor DNA variant allele frequency (VAF) at this position.
Tier
: A tier suggesting the suitability of variants for use in vaccines.
Ref Match
: Whether or not a match for the best peptide to the reference proteome was found.
Evaluation
:
Column to store the evaluation of each variant when evaluating the run in pVACview. Can be
Pending
,
Accept
,
Reject
, or
Review
.
How are the Tiers assigned?
Tiers
Pass
: binding criteria pass AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria pass AND transcript criteria pass AND problematic position criteria pass AND anchor residue criteria pass
PoorBinder
:
binding criteria fail
AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria pass AND transcript criteria pass AND problematic position criteria pass AND anchor residue criteria pass
RefMatch
: binding criteria pass AND
reference match critieria fail
AND allele expression criteria pass AND
vaf clonal criteria pass AND transcript criteria pass AND problematic position criteria pass AND anchor residue criteria pass
Poor Transcript
: binding criteria pass AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria pass AND
transcript criteria fail
AND problematic position criteria pass AND anchor residue criteria pass
LowExpr
: binding criteria pass AND reference match critieria pass AND
allele expression criteria fail
AND
vaf clonal criteria pass AND transcript criteria pass AND problematic position criteria pass AND anchor residue criteria pass
Anchor
: binding criteria pass AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria pass AND transcript criteria pass AND problematic position criteria pass AND
anchor residue criteria fail
Subclonal
: binding criteria pass AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria fail
AND transcript criteria pass AND problematic position criteria pass AND anchor residue criteria pass
ProbPos
: binding criteria pass AND reference match critieria pass AND allele expression criteria pass AND
vaf clonal criteria pass AND transcript criteria pass AND
problematic position criteria fail
AND anchor residue criteria pass
Poor
: Fails two or more criteria
NoExpr
:
((gene expr == 0) OR (RNA VAF == 0)) AND low expression criteria fail
Tiering Criteria Details
Here we list out the exact logic for passing each respective criteria:
Binding Criteria:
1.
(IC50 MT < binding threshold)
AND (
conservative
percentile threshold strategy) / OR (
exploratory
percentile threshold strategy)
2.
%ile MT < percentile threshold
(if a percentile threshold is set)
Reference Match Criteria:
Ref Match == False
Allele Expression Criteria:
(allele expr >= allele expr cutoff) OR (rna_vaf == 'NA') OR (gene_expr == 'NA')
VAF Clonal Criteria:
(dna vaf < vaf subclonal) OR (dna_vaf == 'NA')
Transcript Criteria:
Meet at least one of the criteria specified in the
transcript prioritization strategy
mane_select:
MANE Select == True
canonical:
Canonical == True
tsl:
(TSL != 'NA') AND (TSL < maximum transcript support level)
Problematic Position Criteria:
Prob Pos != None
Anchor Residue Criteria:
1.
(Mutation(s) is at anchor(s)) AND (IC50 WT < binding threshold))
OR
2.
Mutation(s) not or not entirely at anchor(s)
Low Expression Criteria:
(allele expr > 0) OR ((gene expr == 0) AND (RNA Depth > RNA Coverage Cutoff) AND (RNA VAF > RNA vaf cutoff))
Variant Information
The Variant Information panel contains various information for the selected variant.
Reference Matches
When the
--run-reference-proteome-similarity
option is chosen, pVACseq will output a file of found matches of
the epitode candidates in the reference proteome. The
Reference Matches
tab will display the subsequent matches
for the candidate currently being investigated:
The tab shows the best peptide with the variant highlighted in red, the query peptide string which includes the flanking sequence
and the best peptide highlighted in yellow, and a table of reference proteome hits
The Hits table will display the peptide substring that match the query sequence and the genes, transcripts, and Hit IDs of the found matches.
Additional Data
The data displayed in this tab is dependent on the additional data file that you provided in the Upload page. The IC50 MT value and %ile MT values are shown if the app was able to locate the same variant in the data file provided. Values will show up as N/A if IC50 MT or %ile MT values are not provided in the additional file. Additionaly, the Best Peptide of the variant from that file will be listed as well as the HLA Allele the Best Peptide prediction was binding to and the Best Transcript for the prediction.
Variant & Gene Info
This box displays the DNA VAF, RNA VAF, and gene expression values for the variant you have selected for investigation. The genomic information is provided in the format showing the chromosomal location of the variant for further variant analysis such as manual review. We also provide a link out to the variant report provided by OpenCRAVAT. This report will allow users to explore the variant with information regarding: variant annotation, cancer, population allele frequencies, clinical relevance, gene annotation, pathogenicity prediction etc.
Transcript Set Table
Upon selecting a variant for investigation, you may have multiple transcripts covering the region.
These transcripts are grouped into
Trancripts Sets
, based on the good-binding peptides
produced. (Transcripts that produce the exact same set of peptides are grouped together.)
The table also lists the number of transcripts and corresponding peptides in each set (each pair of WT and MT peptides are considered 1 when
counting). A sum of the total expression across all transcripts in each set is also shown.
A light green color is used to highlight the
Transcript Set
producing the Best Peptide for the variant in question.
Transcript Set Detailed Data
Upon selecting a specific transcript set, you can see more details about the exact transcripts that are included.
The
Transcripts in Set
table lists all information regarding each transcript including:
Ensembl Transcript ID, Transcript Expression, MANE Select status, Canonical status, Transcript Support Level, Biotype, CDS Flags,
and Transcript Length.
A light green color is used to highlight the specific
Transcript in Selected Set
that produced the Best Peptide
for the variant in question.
Peptide Table
Upon selecting a specific transcript set, you can also visualize which well-binding peptides are produced from this set. The best peptide is highlighted in light green.
Both, mutant (
MT
) and wildtype (
WT
) sequences are shown, along with either the
lowest
or
median
binding affinities, depending on how you generated the aggregate report.
An
X
is marked for binding affinities higher than the
aggregate_inclusion_binding_threshold
set when generating the aggregate report.
We also include three extra columns, one specifying the mutated position(s) in the peptide, one providing information on any problematic amino acids in the mutant sequence, and one identifying whether the peptide failed the anchor criteria for any of the HLA alleles.
Note that if users wish to utlitize the
problematic positions
feature, they should run the standalone command
pvacseq identify_problematic_amino_acids
or run pVACseq with the
--problematic-amino-acids
option enabled to generate the needed information.
Anchor Heatmap
The Anchor Heatmap tab shows the included MT/WT peptide pairs from the peptide table with anchor probabilities overlayed as a heatmap. The anchor probabilities shown are both allele and peptide length specific. The mutated amino acid is marked in red (for missense mutations) and each MT/WT pair are separated from others using a dotted line. For peptide sequences with no overlaying heatmap, we currently do not have allele-specific predictions in our database.
The Anchor Positions section shows a table of the per-allele, per-length anchor positions calculated from the anchor weights and the specified anchor contribution threshold. For more information on how the anchor positions are calculated, please refer to Advanced Options: Regenerate Tiering section.
The Anchor Weights section shows a table of the per-allele, per-length anchor weights for each peptide position.
For more details and explanations regarding anchor positions and its influence on neoantigen prediction and prioritization, please refer to the next section: Advanced Options: Anchor Contribution
Additional Information
IC50 Plot
By clicking on each MT/WT peptide pair, you can then assess the peptides in more detail by navigating to the
Additional Peptide Information
tab.
There are five different tabs in this section of the app, providing peptide-level details on the MT/WT peptide pair that you have selected.
The
IC50 Plot
tab shows violin plots of the individual IC50-based binding affinity predictions of the MT and WT peptides for HLA
alleles that the MT binds well to. These peptides each have up to 8 binding algorithm scores for Class I alleles or up
to 4 algorithm scores for Class II alleles.
%ile Plot
The %ile Plot tab shows violin plots of the individual percentile-based binding affinity predictions of the MT and WT peptides for HLA alleles that the MT binds well to.
Binding Data
The Binding Data tab shows the specific IC50 and percentile binding affinity predictions generated from each individual algorithm. Each cell shows the IC50 prediction followed by the percentile predictions in parenthesis.
Elution and Immunogenicity Table
The Elution and Immunigenicity Table tab shows presentation and immunogenicity prediction results. This includes algorithms such as NetMHCpanEL/NetMHCIIpanEL, MHCflurryEL (Processing and Presentation), DeepImmuno, and BigMHC (Presentation and Immunogenicity).
Anchor vs Mutation Positions
Neoantigen identification and prioritization relies on correctly predicting whether the presented
peptide sequence can successfully induce an immune response. As the majority of somatic mutations are single nucleotide variants,
changes between wildtype and mutated peptides are typically subtle and require cautious interpretation.
In the context of neoantigen presentation by specific MHC alleles, researchers have noted that a subset of
peptide positions are presented to the T-cell receptor for recognition, while others are responsible for anchoring
to the MHC, making these positional considerations critical for predicting T-cell responses.
Multiple factors should be considered when prioritizing neoantigens, including mutation location, anchor position, predicted MT
and WT binding affinities, and WT/MT fold change, also known as agretopicity.
Examples of the four distinct possible scenarios for a predicted strong MHC binding peptide involving these factors are illustrated
in the figure on the right. There are other possible scenarios where the MT is a poor binder, however those are not listed as
they would not pertain to our goal of neoantigen identification.
Scenario 1
shows the case where the WT is a poor binder and the MT peptide is a strong binder,
containing a mutation at an anchor location. Here, the mutation results in a tighter binding of the MHC and allows for
better presentation and potential for recognition by the TCR. As the WT does not bind (or is a poor binder), this neoantigen
remains a good candidate since the sequence presented to the TCR is novel.
Scenario 2
and
Scenario 3
both have strong binding WT and MT peptides. In
Scenario 2
, the mutation of the peptide is located at a non-anchor location, creating a difference in the sequence participating in TCR
recognition compared to the WT sequence. In this case, although the WT is a strong binder, the neoantigen remains a good candidate
that should not be subject to central tolerance.
However, as shown in
Scenario 3
, there are neoantigen candidates where the mutation is located at the anchor position
and both peptides are strong binders. Although anchor positions can themselves influence TCR recognition, a mutation at a strong
anchor location generally implies that both WT and MT peptides will present the same residues for TCR recognition. As the WT peptide
is a strong binder, the MT neoantigen, while also a strong binder, will likely be subject to central tolerance and should not be
considered for prioritization.
Scenario 4
is similar to the first scenario where the WT is a poor binder. However, in this case, the mutation is
located at a non-anchor position, likely resulting in a different set of residues presented to the TCR and thus making the neoantigen a good candidate.
Anchor Guidance
To summarize, here are the specific criteria for prioritizing (accept) and not prioritizing (reject) a neoantigen candidate:
Note that in all four cases, we are assuming a strong MT binder which means
(MT IC50 < binding threshold)
I:
WT Weak binder:
WT IC50 < binding threshold
II:
WT Strong binder:
WT IC50 > binding threshold
III:
Mutation at Anchor:
set(All mutated positions)
is a subset of
set(Anchor Positions of corresponding HLA allele)
IV: Mutation not at Anchor: There is at least one mutated position between the WT and MT that is not at an anchor position
Scenario 1 :
I + IV
-> Accept
Scenario 2 :
II + IV
-> Accept
Scenario 3 :
II + III
-> Reject
Scenario 4 :
I + III
-> Accept
Reassigning Tiers for all variants after adjusting parameters
The Tier column generated by pVACtools is aimed at helping users group and prioritize neoantigens in a more efficient manner. For details on how Tiering is done, please refer to the Variant Level tutorial tab where we break down each specific Tier and its criteria.
While we try to provide a set of reasonable default parameters, we fully understand the need for flexible changes to the parameters used in the underlying Tiering algorithm. Thus, we provide an Advanced Options tab in our app where users can change the following cutoffs custom to their individual analysis:
Default Anchors vs Allele-specific AnchorsBy default, pVACtools considers positions 1, 2, n-1, and n to be anchors for an n-mer allele. However, a recent study has shown that anchors should be considered on an allele-specific basis and different anchor patterns exist among HLA alleles. Here, we provide users with the option to utilize allele-specific anchors when generating the Anchor Tier by selecting the appropriate check box. However, to objectively determine which positions are anchors for each individual allele, the users need to set a contribution percentage threshold (X).
Per anchor calculation results from the described computational workflow in the cited paper, each position of the n-mer peptide is assigned a score based on how its binding to a certain HLA allele was influenced by mutations. These scores can then be used to calculate the relative contribution of each position to the overall binding affinity of the peptide. Given the contribution threshold X, we rank the normalized score across the peptide in descending order (e.g. [2,9,1,3,2,8,7,6,5] for a 9-mer peptide) and start summing the scores from top to bottom. Positions that together account for X% of the overall binding affinity change (e.g. 2,9,1) will be assigned as anchor locations for tiering purposes.
However, we recommend users also navigating to the Anchor Heatmap Tab in the peptide level description for a less binary approach.
Binding ThresholdIC50 cutoff for a peptide to be considered a strong binder.
Allele-specific binding thresholdsWhen this box is checked, use allele-specific binding thresholds, as defined by IEDB, instead of the binding threshold set above. For alleles where no specific threshold is defined, the binding threshold set above is used as a fallback.
Percentile ThresholdPercentile cutoff for a peptide to be considered a strong binder.
Percentile Threshold Strategy
When specifying a percentile threshold, this parameter determines how it is evaluated. If it is set to
conservative
, the peptide needs
to meet BOTH the binding threshold AND the percentile threshold in order to be considered a good binder. If it is set to
exploratory
, EITHER the binding threshold OR percentile threshold will need to be met.
VAF cutoff that is taken into account when deciding subclonal variants. Note that variants with a DNA VAF lower than half of the clonal VAF cutoff will be considered subclonal (e.g. setting a 0.6 clonal VAF cutoff means anything under 0.3 VAF is subclonal).
Allele ExpressionAllele expression cutoff for a peptide to be considered expressed. Note for each variant, the allele expression is calculated by multiplying gene expression and RNA VAF.
Transcript Prioritization Strategy
Specify the list of criteria to evaluate to determine whether or not the Best Transcript is a good transcript. If
canonical
is in the
list, check whether the Canonical value is True. If
mane_select
is in the list, check whether the MANE Select value is True. If
tsl
is in the list, check whether the TSL value meets the Maximum Transcript Support Level cutoff. The Best Transcript needs to pass at
least one of the specified criteria in order to be considered a good transcript.
The threshold to use for evaluating a transcript on itse Ensembl transcript support level (TSL).
Top Score Sorting MetricSpecify the metric that should be used as the primary sort criteria for sorting candidates within each tier.
Original Parameters
In this box, we provide users with the original parameters they had used to generate the currently loaded aggregate report and metrics file.
Note that the app will keep track of your peptide evaluations and comments accordingly even when changing or reseting the parameters.
If you see a parameter in the original parameter box but did not see an option to change it in the advanced options section, it is likely that you
will be required to rerun the
pvacseq generate-aggregate-report
command. This is likely due to the current metrics file not
having the necessary peptide information to perform this request.
Current Parameters
In this box, we provide users with the tiering parameters that currently applied to the aggregate report. This not only allows users to compare their current parameters (if changed) with the original parameters.
Resetting Parameters
The reset button allows the user to restore the original tiering when desired.
Module for Exploring NeoFox Annotated Neoantigens
The one required file should end with the suffix "_neoantigen_candidates_annotated.tsv". The module expects all all NeoFox annotated features to be in in the file and can handle input with other annotations you might append to the neoantigen candidates.
Features
Annotated Neoantigen Table
The annotated neoantigen table is generated as output from NeoFox and includes many annotations based on published neoantigen features. You can page through the candidates, sort by any feature, and select one or more candidates for further investgation. We have marked the features we find most informative with an asterisk. These columns are selected by default but additional columns can be selected using the "Column Visibility" dropdown.
Colored heatmap cell backgrounds on binding affinity and rank columns indicate where the value falls in comparison to the default 500 nM binding affinity and 0.5 percentile thresholds, respectively. Green background colors indicate a value below the threshold while yellow to red colors indicate a progressively higher value above the threshold. Horizontal barplot backgrounds on the expression and VAF columns reflect how close the values are to the "ideal" values of 50 and 1, respectively.
Comparative Violin Plots
You can understand how selected candidates relate to the the rest of the dataset using the comparative violin plots. You can select as many candidates as you would like which will then be highlighted in red in the violin plots. You can also select up to six features to view at a time. We have pre-selected five features which we found informative.
Dynamic Scatter Plot
You can also further investigate the data using the dynamic scatter plot where you can choose any feature to be the X-axis, Y-axis, color, or size variable. The X and Y scale can be transformed and a range of values subsetted. The color represents the minimum and maximum values can also be changed to any HEX value.
To view information about different points on the plot simply mouse over individual points. You can also export the current scatter plot by using the camera icon at the top right corner of the plot.
Evaluation and Commenting
The evaluation buttons at the right of each candidate row can be used to capture the final decision on whether to accept, reject, or further review the candidate. The total counts for each type of evaluation are displayed in the "Peptide Evaluation Overview" panel.
You are able to leave a comment on all selected candidates by using the form in the panel on the top right of the page. This panel also displays the comments left on each selected candidate. Both the selected evaluation and comment are included in the exported table.
Exporting
After investigating and evaluating your candidates, you can export the main table, including the final evaluation and comment for each candidate. After browsing to the "Export Data" tab, click the "Download as TSV" or "Download as excel" button to download the table in your desired file format.
Module for Exploring Any Annotated Neoantigens
The custom module boasts the most flexibility for viewing your data, since there are no required features that are expected to be in the file.
We provide three examples of neoantigen prediction pipeline output data: Vaxrank, NeoPredPipe, and antigen.garnish.2
When you upload your file, you can then choose how to visualize the data by selecting which feature from your input you would like to group and sort candidates by. The feature you choose to group by will allow you to explore candidates that are simliar to one another in a separate table. For example, to mimic the pVACseq Module grouping you could select to group by variant. The order of the candidates in each grouping is determined by the numeric feature you choose to sort by. Canidates within the pVACseq Module are sorted by best binders. Finally, you can select what features to display for each group of peptides, the default selection is all features.
Features
Overview of Neoantigen Features
The Overview of Neantigen Features table displays the groups of candidates as designated by the feature you specify. The top candidate of the group according to the sort by feature is shown in the table. To investage the candidates within the group, click the Investigate button.
Detailed Data
The Detailed Data table shows you all the candidates within the group so that you can compared them to one another. This table will only display the features that you selected on the upload page.
Dynamic Scatter Plot
You can also further investigate the data using the dynamic scatter plot where you can choose any feature to be the X-axis, Y-axis, color, or size variable. The X and Y scale can be transformed and a range of values subsetted. The color represents the minimum and maximum values can also be changed to any HEX value.
To view information about different points on the plot simply mouse over individual points. You can also export the current scatter plot by using the camera icon at the top right corner of the plot.
Option 1: View NeoFox demo data
Please wait a couple seconds after clicking for the data to load.
Option 2: Upload your own neofox data files
(Required) Please upload your neofox output file. This file should be a table generated by NeoFox with the suffix “_neoantigen_candidates_annotated.tsv“
NeoFox (NEOantigen Feature toolbOX)
NeoFox (NEOantigen Feature toolbOX) is a python package that annotates a given set of neoantigen candidate sequences with relevant neoantigen features.
The tool covers neoepitope prediction by MHC binding and ligand prediction, similarity/foreignness of a neoepitope candidate sequence, combinatorial features and machine learning approaches by running a wide range of published toolsets on the given input data. For more detailed information on the specific neoantigen-related algorithms and how to generate your own NeoFox results, please refer to the link below:
Peptide Evaluation Overview
Add Comments for last selected variant(s)
Comment:
Annotated Neoantigen Candidates using NeoFox
Comparative Violin Plots
Violin Plots showing distribution of various neoantigen features for selected variants.
Dynamic Scatter Plot
Scatter plot to explore characteristics of data
Option 1: View Demo data
After clicking the "Load demo data" button, select your desired grouping and sorting parameters in the "Choose How to Visualize Data" panel and click "Visualize". Please wait a couple seconds for the data to load.
Option 2: View NeoPredPipe demo data
After clicking the "Load demo data" button, select your desired grouping and sorting parameters in the "Choose How to Visualize Data" panel and click "Visualize". Please wait a couple seconds for the data to load.
Option 3: View antigen.garnish demo data
After clicking the "Load demo data" button, select your desired grouping and sorting parameters in the "Choose How to Visualize Data" panel and click "Visualize". Please wait a couple seconds for the data to load.
Option 4: Upload your own custom data files
(Required) Please upload your TSV file.
Choose How to Visualize Data
Group peptides together by a certain feature. For example, grouping by variant would allow user to explore all proposed peptides for one variant at a time.
Order peptides by a certain feature. For example, ordering peptides by binding scores to find the best binders.
Choose what features you would like to consider for each group of peptides.
Example Neoantigen Prediction Pipelines
Vaxrank: A computational tool for designing personalized cancer vaccines
Therapeutic vaccines targeting mutant tumor antigens (“neoantigens”) are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/openvax/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.
NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline
NeoPredPipe (Neoantigen Prediction Pipeline) is offered as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. This tool allows a user to process neoantigens predicted from single- or multi-region vcf files using ANNOVAR and netMHCpan.
antigen.garnish.2: Tumor neoantigen prediction
Human and mouse ensemble tumor neoantigen prediction from SNVs and complex variants. Immunogenicity filtering based on the Tumor Neoantigen Selection Alliance (TESLA).
Overview of Neoantigen Features
Detailed Data
Dynamic Scatter Plot
Scatter plot to explore characteristics of data
Bug reports or feature requests can be submitted on the
pVACtools Github page.
You may also contact us by email at
help@pvactools.org
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